Geodesic
Improved Free-form Modelling of Scattered Data by Dynamic Neural Networks
Journal for geometry and graphics, Tome 3 (1999) no. 2, pp. 177-182.

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The aim of this paper is to improve the method of modelling scattered data by free-from surfaces presented in a former paper. In that method a neural network was used for ordering the data and forming a quadrilateral control grid from the scattered points, hence the standard free-form methods like Bezier-surface or NURBS could be applied to approximate or interpolate the data. Instead of the original artificial neural network, which has been used for ordering the data, now a recent development, the dynamic version of this neural network is applied. Hence the preprocess of ordering the spatial scattered data is based on the neural network, the improvement of the network yields a much faster and more reliable algorithm. 
@article{JGG_1999_3_2_a3,
     author = {L. Varady and M. Hoffmann and E. Kovacs},
     title = {Improved {Free-form} {Modelling} of {Scattered} {Data} by {Dynamic} {Neural} {Networks}},
     journal = {Journal for geometry and graphics},
     pages = {177--182},
     publisher = {mathdoc},
     volume = {3},
     number = {2},
     year = {1999},
     url = {http://geodesic.mathdoc.fr/item/JGG_1999_3_2_a3/}
}
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L. Varady; M. Hoffmann; E. Kovacs. Improved Free-form Modelling of Scattered Data by Dynamic Neural Networks. Journal for geometry and graphics, Tome 3 (1999) no. 2, pp. 177-182. http://geodesic.mathdoc.fr/item/JGG_1999_3_2_a3/